You may think of tuberculosis (TB) as a deadly infection of the past, yet it remains one of the leading causes of mortality in many countries today. While TB exists in every part of the world, 80% of cases and deaths occur in low- and middle-income countries. Epidemiologists at KIT Institute have spent over a decade evaluating the effectiveness of TB control strategies to find and treat people with TB. Building on this knowledge, KIT is conceptualizing new methods to estimate TB burden to identify high risk groups. Although TB prevalence surveys are still the best approach to estimate a country’s TB burden, they come with certain limitations. This blog advocates for using Active Case Finding (ACF) data to better understand the TB burden among high-risk populations. 

Ending TB by 2030 

TB caused an estimated 10,7 million individuals to fall ill in 2023, causing 1,2 million deaths worldwide. Because of this high incidence, ending the global TB epidemic by 2030 is one of the health targets of the United Nations Sustainable Development Goals (SDGs) and a strategic priority of the World Health Organization (WHO). People who become infected with TB can develop symptoms like a prolonged cough, weight loss, fatigue and fever. When left untreated, TB can be fatal. In fact, without treatment, more than 50% of people with the disease are expected to die, making it the leading cause of death from infection worldwide. The disease comes with an additional burden as it often impacts people in their most economically productive years. Households affected by TB may face loss of livelihoods due to loss of income, unemployment, and financial insecurity from out-of-pocket medical costs.  

Surveys to estimate and understand TB burden  

Currently, prevalence surveys are the best-known method to estimate TB burden, and are done so in a representative manner for large populations. These surveys are extremely expensive and resource intensive, because a lot of people, time and equipment are needed to conduct such a large study. For this reason they are carried out once every 10 years on average in countries where the expected number of TB cases is relatively high. Also, prevalence surveys can only reliably estimate the number of TB cases at the national level, overlooking valuable insights into subnational variation in the burden of TB. 

Why active case finding data are valuable  

Although national prevalence estimates enable us to obtain a general sense of TB burden in the community, it fails to enlighten TB program staff about where to increase programmatic efforts for screening and diagnosis for resource prioritization sub nationally. More and more, disease program staff are recognizing the potential value of having subnational TB burden estimates, which can be generated using ACF data.

While the vast majority of people diagnosed with TB are identified through passive case-finding, which is when individuals proactively seek care in health facilities, ACF involves screening for TB outside health facilities among at-risk populations such as low-income communities with limited access to healthcare.

ACF activities are often necessary to reach and diagnose people with TB in communities, many of whom may not be able to access health services due in part to geographic and/or socioeconomic obstacles, and would remain undiagnosed. This leads to continued illness for a person with TB, and potential transmission to their family members and communities as it is a respiratory infection. 

ACF data contain a lot of information about TB burden in the screened at-risk populations. When these outcomes are triangulated with 1) information about how and 2) where the ACF events were conducted, and with 3) TB program data, this helps to shed light on TB burden in the communities where ACF took place. 

Increasingly, data outside of prevalence surveys, such as data collected through ACF campaigns, are being considered as a means of understanding TB burden sub nationally. In some countries, ACF is already conducted on a daily basis by e.g. community health workers or teams of medical staff in vans, who are collecting screening, testing and diagnosis data about people who come to ACF events. As we continue to use ACF data to improve TB treatment coverage, we gain insights into how these can be used to estimate and monitor TB burden. While many questions remain regarding how we can best use ACF data to estimate TB disease burden, we are starting to close these knowledge gaps. 

KIT work and the future 

KIT has lots of experience analysing ACF data with a specific focus on how these data relate to local TB notifications, TB program factors, presence of at-risk populations, and how all of these factors vary across space and time. KIT leverages this expertise to improve its expertise in modelling subnational TB burden, as part of its contribution in the fight against TB. The insights provided by ACF data on local TB burden should be seriously considered, as this will ultimately support TB program planners in making evidence-based decisions about where to deliver TB services.  

If you would like to learn more about how ACF can improve your TB control strategy and reduce the number of TB cases in your region, please contact our advisors Christina Mergenthaler, Mirjam Bakker or Ente Rood.